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A novel self-adaptive Ant Colony Optimization algorithm based on Quantum mechanism for Traveling salesman problem(TQACO) is proposed. Firstly, initializing the population of the ant colony with superposition of Q-bit, Secondly, using self-adaptive operator, namely in prophase we use higher probability to explore more search space and to collect useful global information; otherwise in anaphase we use higher probability to accelerate convergence. This mechanism offers the ability to escape from local optima and can self-regulate the production of diverse antibodies. Because of the quantum superposition and rotation it can maintain quite nicely the population diversity than the classical evolutionary algorithm, because of the self-adaptive operator it can obtain more optimal solution and the solution quality is improved significantly. TSP benchmark instances Chn144 results demonstrate the superiority of TQACO in this paper.